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* Tweak conv-rnn model - Fix misplaced zero_grad() - Tweak model hyperparams and optimization algorithm * Fix typo * Add new results * Clean up extraneous code
24 lines
757 B
Markdown
24 lines
757 B
Markdown
## Convolutional RNN
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Implementation based on [[1]](http://dl.acm.org/citation.cfm?id=3098140).
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### Usage
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Run `./getData.sh` to fetch the data. The project structure should now look like this:
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```
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├── conv_rnn/
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│ ├── data/
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│ ├── saves/
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│ └── *.*
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```
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You may then run `python train.py` and `python test.py` for training and testing, respectively. For more options, add the `-h` switch.
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### Empirical results
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Best dev | Test
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-- | --
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52.04359673024523 | 50.85972850678733
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### References
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[1] Chenglong Wang, Feijun Jiang, and Hongxia Yang. 2017. A Hybrid Framework for Text Modeling with Convolutional RNN. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '17).
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